re: 0.005 on http://neuralnetworksanddeeplearning.com/chap3.html

tensorflow.py

import tensorflow as tf

w = tf.Variable([2.0])
b = tf.Variable([2.0])
x = tf.constant([1.0])
y = tf.sigmoid(w*x + b)
y_ = tf.constant([0.0])
cross_entropy = -1*(y_*tf.log(y) + (1-y_)*(tf.log(1-y)))
s = tf.Session()
s.run(tf.initialize_all_variables())
train_step = tf.train.GradientDescentOptimizer(0.005).minimize(cross_entropy)
for i in range(300):
    s.run(train_step)
    print i, s.run(y)
# far too slow

import tensorflow as tf

w = tf.Variable([2.0])
b = tf.Variable([2.0])
x = tf.constant([1.0])
y = tf.sigmoid(w*x + b)
y_ = tf.constant([0.0])
cross_entropy = -1*(y_*tf.log(y) + (1-y_)*(tf.log(1-y)))
s = tf.Session()
s.run(tf.initialize_all_variables())
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
for i in range(300):
    s.run(train_step)
    print i, s.run(y)
# about right